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Dots3-Note Preview is an open-weight mixture-of-experts model from Dots Studio, with 16B active parameters out of 280B total. It is the lightest model in the Dots 3 family and is...
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We present dots.mocr. Beyond achieving state-of-the-art (SOTA) performance in standard multilingual document parsing among models of comparable size, **dots.mocr** excels at converting structured graphics (e.g., charts, UI layouts, scientific figures and etc.) directly into SVG code. Its core capabilities encompass grounding, recognition, semantic understanding, and interactive dialogue. Simultaneously, we are releasing dots.mocr-svg, a variant specifically optimized for robust image-to-SVG parsing tasks. More information can be found in the paper.
**dots.ocr** is a powerful, multilingual document parser that unifies layout detection and content recognition within a single vision-language model while maintaining good reading order. Despite its compact 1.7B-parameter LLM foundation, it achieves state-of-the-art(SOTA) performance. 1. **Powerful Performance:** **dots.ocr** achieves SOTA performance for text, tables, and reading order on OmniDocBench, while delivering formula recognition results comparable to much larger models like Doubao-1.5 and gemini2.5-pro. 2. **Multilingual Support:** **dots.ocr** demonstrates robust parsing capabilities for low-resource languages, achieving decisive advantages across both layout detection and content recognition on our in-house multilingual documents benchmark. 3. **Unified and Simple Architecture:** By leveraging a single vision-language model, **dots.ocr** offers a significantly more streamlined architecture than conventional methods that rely on complex, multi-model pipelines. Switching between tasks is accomplished simply by altering the input prompt, proving that a VLM can achieve competitive detection results compared to traditional detection models like DocLayout-YOLO. 4. **Efficient and Fast Performance:** Built upon a compact 1.7B LLM, **dots.ocr** provides faster inference speeds than many other high-performing models based on larger foundations. > **Notes:** > - The EN, ZH metrics are the end2end evaluation results of OmniDocBench, and Multilingual metric is the end2end evaluation results of dots.ocr-bench.
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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dots3-note preview is the first open-weight model in the dots3 family. It is a Mixture-of-Experts model with 280B total parameters, 16B activated parameters, and support for a context length of up to 512K tokens. The model can understand text, images, video, and audio, and produces text outputs. The dots3 family is designed to include models with different trade-offs among capability, latency, and inference cost. dots3-note preview is the most lightweight member of the family. The commands below target FP8 on one 8-GPU node. BF16 requires more memory. Tune the context length to available memory, concurrency, and input modalities.
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